FilippoMB / Recognition-of-polar-lows-in-Sentinel-1-SAR-images-with-deep-learning

Code implementation for the paper "Recognition of polar lows in Sentinel-1 SAR images with deep learning"

This repository contains an example of how to train the deep learning architecture and how to use the interpretability tools used in the paper Recognition of polar lows in Sentinel-1 SAR images with deep learning.

---

Dataset

The Sentinel-1 maritime mesocyclone dataset is publicly available and can be downloaded here.

---

Installation (with Anaconda)

Create an Anaconda environment using the environment.yml file.

conda env create -f environment.yml

The environment was created on Ubuntu 20.04. For more details of how to create and manage an environment, look here.

---

Pretrained models

The best performing models can be downloaded here.

To use a pretrained model:

import tensorflow as tf
import tensorflow_addons as tfa

model = tf.keras.models.load_model(
    'models/model_1536_F1_095.h5', 
    custom_objects={'AdamW': tfa.optimizers.AdamW})
img = tf.keras.preprocessing.image.load_img(
    "data/test/pos/cffe42_20191012T084028_20191012T084212_mos_rgb.png", 
    target_size=(1536, 1536))
img_array = tf.keras.preprocessing.image.img_to_array(img)

pred = model.predict(tf.expand_dims(img_array, 0))
print("Predicted class: ", pred[0])

---

Model training from scratch

If you want to train the deep learning model from scratch take a look at train_model.py, which provides a simple example of how to train the architecture adopted in our paper. The script downloads automatically the dataset in the data/ folder.

---

Model interpretability

The following notebooks show how to use the interpretability techniques to see what the deep learning model is focusing on:

• GradCAM.ipynb
• Integrated_gradients.ipynb

---

Citation

Consider citing our paper if you are using our dataset in your reasearch

@article{grahn2022recognition,
  title={Recognition of polar lows in Sentinel-1 SAR images with deep learning},
  author={Grahn, Jakob and Bianchi, Filippo Maria},
  journal={arXiv preprint arXiv:2203.16401},
  year={2022}
}